Urban Scene Semantic Segmentation with Low-Cost Coarse Annotation

• URL: http://arxiv.org/abs/2212.07911v1
• Date: Thu, 15 Dec 2022 15:43:42 GMT
• Title: Urban Scene Semantic Segmentation with Low-Cost Coarse Annotation
• Authors: Anurag Das, Yongqin Xian, Yang He, Zeynep Akata and Bernt Schiele
• Abstract summary: coarse annotation is a low-cost but highly effective alternative for training semantic segmentation models. We propose a coarse-to-fine self-training framework that generates pseudo labels for unlabeled regions of coarsely annotated data. Our method achieves a significantly better performance vs annotation cost tradeoff, yielding a comparable performance to fully annotated data with only a small fraction of the annotation budget.
• Score: 107.72926721837726
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: For best performance, today's semantic segmentation methods use large and carefully labeled datasets, requiring expensive annotation budgets. In this work, we show that coarse annotation is a low-cost but highly effective alternative for training semantic segmentation models. Considering the urban scene segmentation scenario, we leverage cheap coarse annotations for real-world captured data, as well as synthetic data to train our model and show competitive performance compared with finely annotated real-world data. Specifically, we propose a coarse-to-fine self-training framework that generates pseudo labels for unlabeled regions of the coarsely annotated data, using synthetic data to improve predictions around the boundaries between semantic classes, and using cross-domain data augmentation to increase diversity. Our extensive experimental results on Cityscapes and BDD100k datasets demonstrate that our method achieves a significantly better performance vs annotation cost tradeoff, yielding a comparable performance to fully annotated data with only a small fraction of the annotation budget. Also, when used as pretraining, our framework performs better compared to the standard fully supervised setting.

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